Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions

• URL: http://arxiv.org/abs/2501.01981v1
• Date: Sun, 29 Dec 2024 09:56:03 GMT
• Title: Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions
• Authors: Yash Agrawal, Srinidhi Balasubramanian, Rahul Meena, Rohail Alam, Himanshu Malviya, Rohini P,
• Abstract summary: The study mainly focuses on three pre-trained CNNs, namely LeNet, VGG-16, and MobileNet. The findings reveal that MobileNet outperforms the other two models in terms of accuracy, achieving a validation accuracy of 95.94% and validation loss of 0.129.
• Score: 0.13194391758295113
• License:
• Abstract: This research paper delves into the development of an Optical Character Recognition (OCR) system for the recognition of Ashokan Brahmi characters using Convolutional Neural Networks. It utilizes a comprehensive dataset of character images to train the models, along with data augmentation techniques to optimize the training process. Furthermore, the paper incorporates image preprocessing to remove noise, as well as image segmentation to facilitate line and character segmentation. The study mainly focuses on three pre-trained CNNs, namely LeNet, VGG-16, and MobileNet and compares their accuracy. Transfer learning was employed to adapt the pre-trained models to the Ashokan Brahmi character dataset. The findings reveal that MobileNet outperforms the other two models in terms of accuracy, achieving a validation accuracy of 95.94% and validation loss of 0.129. The paper provides an in-depth analysis of the implementation process using MobileNet and discusses the implications of the findings. The use of OCR for character recognition is of significant importance in the field of epigraphy, specifically for the preservation and digitization of ancient scripts. The results of this research paper demonstrate the effectiveness of using pre-trained CNNs for the recognition of Ashokan Brahmi characters.

Related papers

• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models. In this paper, we investigate how detection performance varies across model backbones, types, and datasets. We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Optimizing the Neural Network Training for OCR Error Correction of Historical Hebrew Texts [0.934612743192798]
  This paper proposes an innovative method for training a light-weight neural network for Hebrew OCR post-correction using significantly less manually created data. An analysis of historical OCRed newspapers was done to learn common language and corpus-specific OCR errors.
  arXiv  Detail & Related papers  (2023-07-30T12:59:06Z)
• Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing [76.72662577101988]
  This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately classify defects in an image dataset from AM and second, applying active learning techniques to the developed classification model. This allows the construction of a human-in-the-loop mechanism to reduce the size of the data required to train and generate training data.
  arXiv  Detail & Related papers  (2023-07-14T14:36:58Z)
• Text recognition on images using pre-trained CNN [2.191505742658975]
  The recognition is trained by using Chars74K dataset and the best model results then tested on some samples of IIIT-5K-Dataset. The research model has an accuracy of 97.94% for validation data, 98.16% for test data, and 95.62% for the test data from IIIT-5K-Dataset.
  arXiv  Detail & Related papers  (2023-02-10T08:09:51Z)
• A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes [58.633364000258645]
  We call this dataset RIVAL10 consisting of roughly $26k$ instances over $10$ classes. We evaluate the sensitivity of a broad set of models to noise corruptions in foregrounds, backgrounds and attributes. In our analysis, we consider diverse state-of-the-art architectures (ResNets, Transformers) and training procedures (CLIP, SimCLR, DeiT, Adversarial Training)
  arXiv  Detail & Related papers  (2022-01-26T06:31:28Z)
• Semantic-Aware Generation for Self-Supervised Visual Representation Learning [116.5814634936371]
  We advocate for Semantic-aware Generation (SaGe) to facilitate richer semantics rather than details to be preserved in the generated image. SaGe complements the target network with view-specific features and thus alleviates the semantic degradation brought by intensive data augmentations. We execute SaGe on ImageNet-1K and evaluate the pre-trained models on five downstream tasks including nearest neighbor test, linear classification, and fine-scaled image recognition.
  arXiv  Detail & Related papers  (2021-11-25T16:46:13Z)
• Influence of image noise on crack detection performance of deep convolutional neural networks [0.0]
  Much research has been conducted on classifying cracks from image data using deep convolutional neural networks. This paper will investigate the influence of image noise on network accuracy. AlexNet was selected as the most efficient model based on the proposed index.
  arXiv  Detail & Related papers  (2021-11-03T09:08:54Z)
• Learning Co-segmentation by Segment Swapping for Retrieval and Discovery [67.6609943904996]
  The goal of this work is to efficiently identify visually similar patterns from a pair of images. We generate synthetic training pairs by selecting object segments in an image and copy-pasting them into another image. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset.
  arXiv  Detail & Related papers  (2021-10-29T16:51:16Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)
• Exploiting the relationship between visual and textual features in social networks for image classification with zero-shot deep learning [0.0]
  In this work, we propose a classifier ensemble based on the transferable learning capabilities of the CLIP neural network architecture. Our experiments, based on image classification tasks according to the labels of the Places dataset, are performed by first considering only the visual part. Considering the associated texts to the images can help to improve the accuracy depending on the goal.
  arXiv  Detail & Related papers  (2021-07-08T10:54:59Z)
• Application of Facial Recognition using Convolutional Neural Networks for Entry Access Control [0.0]
  The paper focuses on solving the supervised classification problem of taking images of people as input and classifying the person in the image as one of the authors or not. Two approaches are proposed: (1) building and training a neural network called WoodNet from scratch and (2) leveraging transfer learning by utilizing a network pre-trained on the ImageNet database. The results are two models classifying the individuals in the dataset with high accuracy, achieving over 99% accuracy on held-out test data.
  arXiv  Detail & Related papers  (2020-11-23T07:55:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.